Iterative image reconstruction approaches are widespread in the field of medical image reconstruction, such as, for example, the reconstruction of PET, and/or PET/CT images. Compared to analytical (one-pass) approaches, iterative image reconstruction techniques allow for the statistical nature of the acquired data to be taken into account, in order to reduce artefacts that degrade the image quality. For each iteration, the acquired detector signal is compared to the application of a system model to a current estimate of the image data, until a predefined stop criterion is reached.
Such an approach is particularly suitable to the reconstruction of PET tracer signals, where, owing to the nature of PET signal detection, the signal to noise ratio is low compared to other imaging modalities. An iterative approach can lead to the emergence of clusters of noise in the image as the reconstruction progresses.
Present image reconstruction approaches often do not take into account that the image data represents a biological structure, and so opportunities for improving the reconstructed image are, currently, missed.
The article “LBP-based penalized weighted least-squares approach to low-dose cone-beam computed tomography reconstruction” by Ma, et. al., published in Proc. SPIE Vol. 9033 903336-1, “Medical Imaging 2014: Physics of Medical Imaging” edited by Whiting, et. al., doi: 10.1117/12.2043289 concerns an iterative image reconstruction approach. In this approach, a least-squares reconstruction is provided having a penalization term. Such approaches can, however, be further improved.